Overview

Dataset statistics

Number of variables9
Number of observations419403
Missing cells0
Missing cells (%)0.0%
Duplicate rows91
Duplicate rows (%)< 0.1%
Total size in memory32.0 MiB
Average record size in memory80.0 B

Variable types

Numeric9

Alerts

Dataset has 91 (< 0.1%) duplicate rowsDuplicates
Consumption_MW is highly overall correlated with Coal_MW and 2 other fieldsHigh correlation
Coal_MW is highly overall correlated with Consumption_MWHigh correlation
Gas_MW is highly overall correlated with Consumption_MW and 1 other fieldsHigh correlation
Production_MW is highly overall correlated with Consumption_MW and 1 other fieldsHigh correlation
Wind_MW has 27315 (6.5%) zerosZeros
Solar_MW has 219493 (52.3%) zerosZeros
Biomass_MW has 183783 (43.8%) zerosZeros

Reproduction

Analysis started2023-01-27 06:46:09.173821
Analysis finished2023-01-27 06:46:28.821650
Duration19.65 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

Consumption_MW
Real number (ℝ)

Distinct5515
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6608.5451
Minimum44
Maximum26209
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-01-27T01:46:28.919826image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile5078
Q15834
median6578
Q37279
95-th percentile8388
Maximum26209
Range26165
Interquartile range (IQR)1445

Descriptive statistics

Standard deviation1007.541
Coefficient of variation (CV)0.15246034
Kurtosis-0.049936456
Mean6608.5451
Median Absolute Deviation (MAD)724
Skewness0.26206434
Sum2.7716436 × 109
Variance1015138.9
MonotonicityNot monotonic
2023-01-27T01:46:29.047987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6588 347
 
0.1%
6482 255
 
0.1%
6722 215
 
0.1%
6686 203
 
< 0.1%
6795 202
 
< 0.1%
6727 202
 
< 0.1%
6581 201
 
< 0.1%
6542 201
 
< 0.1%
6770 199
 
< 0.1%
6578 198
 
< 0.1%
Other values (5505) 417180
99.5%
ValueCountFrequency (%)
44 1
< 0.1%
47 1
< 0.1%
94 1
< 0.1%
95 1
< 0.1%
3666 1
< 0.1%
3667 1
< 0.1%
3698 1
< 0.1%
3709 1
< 0.1%
3713 1
< 0.1%
3714 2
< 0.1%
ValueCountFrequency (%)
26209 1
< 0.1%
21007 1
< 0.1%
9865 1
< 0.1%
9826 1
< 0.1%
9807 1
< 0.1%
9784 1
< 0.1%
9766 1
< 0.1%
9739 1
< 0.1%
9729 1
< 0.1%
9721 1
< 0.1%

Coal_MW
Real number (ℝ)

Distinct3840
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2257.7754
Minimum-485
Maximum5702
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size6.4 MiB
2023-01-27T01:46:29.177529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-485
5-th percentile1346
Q11834
median2196
Q32652.5
95-th percentile3340
Maximum5702
Range6187
Interquartile range (IQR)818.5

Descriptive statistics

Standard deviation610.83659
Coefficient of variation (CV)0.27054799
Kurtosis-0.081301491
Mean2257.7754
Median Absolute Deviation (MAD)403
Skewness0.30978281
Sum9.4691776 × 108
Variance373121.34
MonotonicityNot monotonic
2023-01-27T01:46:29.298138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2593 395
 
0.1%
2088 344
 
0.1%
2040 342
 
0.1%
2073 340
 
0.1%
1917 338
 
0.1%
2150 335
 
0.1%
2134 334
 
0.1%
2001 334
 
0.1%
2081 333
 
0.1%
2105 333
 
0.1%
Other values (3830) 415975
99.2%
ValueCountFrequency (%)
-485 1
 
< 0.1%
-43 1
 
< 0.1%
50 2
< 0.1%
357 1
 
< 0.1%
358 4
< 0.1%
359 4
< 0.1%
360 2
< 0.1%
362 3
< 0.1%
364 4
< 0.1%
365 2
< 0.1%
ValueCountFrequency (%)
5702 1
< 0.1%
5338 1
< 0.1%
4408 1
< 0.1%
4395 1
< 0.1%
4383 1
< 0.1%
4370 1
< 0.1%
4369 1
< 0.1%
4368 1
< 0.1%
4365 1
< 0.1%
4353 1
< 0.1%

Gas_MW
Real number (ℝ)

Distinct2300
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1008.1513
Minimum-414
Maximum2666
Zeros2
Zeros (%)< 0.1%
Negative1
Negative (%)< 0.1%
Memory size6.4 MiB
2023-01-27T01:46:29.426017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-414
5-th percentile366
Q1604
median984
Q31329
95-th percentile1871
Maximum2666
Range3080
Interquartile range (IQR)725

Descriptive statistics

Standard deviation469.65867
Coefficient of variation (CV)0.4658613
Kurtosis-0.52466816
Mean1008.1513
Median Absolute Deviation (MAD)365
Skewness0.41476628
Sum4.2282167 × 108
Variance220579.26
MonotonicityNot monotonic
2023-01-27T01:46:29.542316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
416 778
 
0.2%
417 702
 
0.2%
415 638
 
0.2%
400 592
 
0.1%
418 576
 
0.1%
398 569
 
0.1%
397 567
 
0.1%
424 564
 
0.1%
399 556
 
0.1%
423 553
 
0.1%
Other values (2290) 413308
98.5%
ValueCountFrequency (%)
-414 1
 
< 0.1%
0 2
 
< 0.1%
29 1
 
< 0.1%
33 1
 
< 0.1%
116 1
 
< 0.1%
120 9
< 0.1%
121 2
 
< 0.1%
128 1
 
< 0.1%
129 8
< 0.1%
130 13
< 0.1%
ValueCountFrequency (%)
2666 1
< 0.1%
2660 1
< 0.1%
2659 1
< 0.1%
2651 1
< 0.1%
2506 1
< 0.1%
2504 2
< 0.1%
2501 1
< 0.1%
2499 1
< 0.1%
2463 1
< 0.1%
2458 2
< 0.1%

Hidroelectric_MW
Real number (ℝ)

Distinct4221
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1840.6141
Minimum0
Maximum4728
Zeros21
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-01-27T01:46:29.667490image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile669
Q11262
median1809
Q32395
95-th percentile3100
Maximum4728
Range4728
Interquartile range (IQR)1133

Descriptive statistics

Standard deviation754.03004
Coefficient of variation (CV)0.40966222
Kurtosis-0.56491018
Mean1840.6141
Median Absolute Deviation (MAD)566
Skewness0.18254896
Sum7.7195906 × 108
Variance568561.3
MonotonicityNot monotonic
2023-01-27T01:46:29.789137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2476 359
 
0.1%
1550 239
 
0.1%
1573 238
 
0.1%
2012 232
 
0.1%
1913 229
 
0.1%
1621 229
 
0.1%
1435 228
 
0.1%
1505 228
 
0.1%
1649 227
 
0.1%
1774 226
 
0.1%
Other values (4211) 416968
99.4%
ValueCountFrequency (%)
0 21
< 0.1%
53 1
 
< 0.1%
56 1
 
< 0.1%
58 1
 
< 0.1%
59 2
 
< 0.1%
60 3
 
< 0.1%
85 1
 
< 0.1%
87 2
 
< 0.1%
90 1
 
< 0.1%
91 1
 
< 0.1%
ValueCountFrequency (%)
4728 1
< 0.1%
4706 1
< 0.1%
4700 1
< 0.1%
4692 1
< 0.1%
4687 1
< 0.1%
4680 1
< 0.1%
4668 1
< 0.1%
4664 1
< 0.1%
4663 1
< 0.1%
4656 1
< 0.1%

Nuclear_MW
Real number (ℝ)

Distinct879
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1320.4078
Minimum0
Maximum1450
Zeros129
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-01-27T01:46:29.917763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile702
Q11377
median1403
Q31419
95-th percentile1427
Maximum1450
Range1450
Interquartile range (IQR)42

Descriptive statistics

Standard deviation223.18161
Coefficient of variation (CV)0.16902476
Kurtosis4.0827664
Mean1320.4078
Median Absolute Deviation (MAD)19
Skewness-2.4101529
Sum5.5378299 × 108
Variance49810.03
MonotonicityNot monotonic
2023-01-27T01:46:30.040266image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1421 11618
 
2.8%
1422 11318
 
2.7%
1423 11129
 
2.7%
1420 11038
 
2.6%
1419 10593
 
2.5%
1424 10450
 
2.5%
1418 9729
 
2.3%
1425 9619
 
2.3%
1417 8851
 
2.1%
1426 8417
 
2.0%
Other values (869) 316641
75.5%
ValueCountFrequency (%)
0 129
< 0.1%
37 1
 
< 0.1%
39 4
 
< 0.1%
40 3
 
< 0.1%
44 1
 
< 0.1%
45 1
 
< 0.1%
47 1
 
< 0.1%
49 1
 
< 0.1%
70 1
 
< 0.1%
83 1
 
< 0.1%
ValueCountFrequency (%)
1450 1
 
< 0.1%
1443 1
 
< 0.1%
1441 3
 
< 0.1%
1440 11
 
< 0.1%
1439 13
 
< 0.1%
1438 29
 
< 0.1%
1437 52
 
< 0.1%
1436 113
 
< 0.1%
1435 252
0.1%
1434 374
0.1%

Wind_MW
Real number (ℝ)

Distinct2828
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501.87965
Minimum-521
Maximum7944
Zeros27315
Zeros (%)6.5%
Negative11515
Negative (%)2.7%
Memory size6.4 MiB
2023-01-27T01:46:30.173808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-521
5-th percentile0
Q164
median272
Q3733
95-th percentile1804
Maximum7944
Range8465
Interquartile range (IQR)669

Descriptive statistics

Standard deviation585.6012
Coefficient of variation (CV)1.166816
Kurtosis1.7646362
Mean501.87965
Median Absolute Deviation (MAD)247
Skewness1.5177154
Sum2.1048983 × 108
Variance342928.77
MonotonicityNot monotonic
2023-01-27T01:46:30.290354image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 27315
 
6.5%
-1 3060
 
0.7%
-2 2222
 
0.5%
3 1655
 
0.4%
1 1559
 
0.4%
2 1531
 
0.4%
5 1390
 
0.3%
4 1383
 
0.3%
-3 1379
 
0.3%
6 1332
 
0.3%
Other values (2818) 376577
89.8%
ValueCountFrequency (%)
-521 2
 
< 0.1%
-26 3
 
< 0.1%
-25 18
 
< 0.1%
-24 32
 
< 0.1%
-23 34
< 0.1%
-22 57
< 0.1%
-21 41
< 0.1%
-20 54
< 0.1%
-19 52
< 0.1%
-18 84
< 0.1%
ValueCountFrequency (%)
7944 1
< 0.1%
2806 1
< 0.1%
2803 1
< 0.1%
2802 1
< 0.1%
2800 2
< 0.1%
2799 1
< 0.1%
2798 2
< 0.1%
2797 1
< 0.1%
2796 1
< 0.1%
2795 1
< 0.1%

Solar_MW
Real number (ℝ)

Distinct857
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.445168
Minimum-6
Maximum859
Zeros219493
Zeros (%)52.3%
Negative78275
Negative (%)18.7%
Memory size6.4 MiB
2023-01-27T01:46:30.414970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-6
5-th percentile-1
Q10
median0
Q316
95-th percentile492
Maximum859
Range865
Interquartile range (IQR)16

Descriptive statistics

Standard deviation161.3681
Coefficient of variation (CV)2.2906908
Kurtosis5.5075357
Mean70.445168
Median Absolute Deviation (MAD)0
Skewness2.5121392
Sum29544915
Variance26039.664
MonotonicityNot monotonic
2023-01-27T01:46:30.531004image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 219493
52.3%
-1 78214
 
18.6%
1 3468
 
0.8%
2 1704
 
0.4%
3 1322
 
0.3%
4 1189
 
0.3%
5 1011
 
0.2%
6 915
 
0.2%
7 912
 
0.2%
8 854
 
0.2%
Other values (847) 110321
26.3%
ValueCountFrequency (%)
-6 1
 
< 0.1%
-4 8
 
< 0.1%
-3 41
 
< 0.1%
-2 11
 
< 0.1%
-1 78214
 
18.6%
0 219493
52.3%
1 3468
 
0.8%
2 1704
 
0.4%
3 1322
 
0.3%
4 1189
 
0.3%
ValueCountFrequency (%)
859 1
< 0.1%
858 1
< 0.1%
854 1
< 0.1%
852 2
< 0.1%
851 1
< 0.1%
848 1
< 0.1%
847 1
< 0.1%
845 1
< 0.1%
844 1
< 0.1%
843 1
< 0.1%

Biomass_MW
Real number (ℝ)

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.365593
Minimum0
Maximum110
Zeros183783
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-01-27T01:46:30.653624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median33
Q355
95-th percentile67
Maximum110
Range110
Interquartile range (IQR)55

Descriptive statistics

Standard deviation27.424163
Coefficient of variation (CV)0.93388758
Kurtosis-1.7138335
Mean29.365593
Median Absolute Deviation (MAD)33
Skewness0.036152394
Sum12316018
Variance752.08472
MonotonicityNot monotonic
2023-01-27T01:46:30.770701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 183783
43.8%
33 14475
 
3.5%
57 9318
 
2.2%
52 9217
 
2.2%
55 9201
 
2.2%
58 9046
 
2.2%
61 8868
 
2.1%
64 7630
 
1.8%
56 7551
 
1.8%
53 7520
 
1.8%
Other values (91) 152794
36.4%
ValueCountFrequency (%)
0 183783
43.8%
10 3
 
< 0.1%
11 33
 
< 0.1%
12 72
 
< 0.1%
13 14
 
< 0.1%
14 26
 
< 0.1%
15 13
 
< 0.1%
16 104
 
< 0.1%
17 230
 
0.1%
18 68
 
< 0.1%
ValueCountFrequency (%)
110 4
 
< 0.1%
109 14
< 0.1%
108 11
< 0.1%
107 11
< 0.1%
106 3
 
< 0.1%
105 5
 
< 0.1%
104 3
 
< 0.1%
103 3
 
< 0.1%
102 3
 
< 0.1%
100 1
 
< 0.1%

Production_MW
Real number (ℝ)

Distinct6936
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7028.7707
Minimum0
Maximum11295
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.4 MiB
2023-01-27T01:46:30.900789image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5208
Q16177
median6973
Q37820
95-th percentile9058
Maximum11295
Range11295
Interquartile range (IQR)1643

Descriptive statistics

Standard deviation1169.3226
Coefficient of variation (CV)0.16636232
Kurtosis-0.31859478
Mean7028.7707
Median Absolute Deviation (MAD)821
Skewness0.23015593
Sum2.9478875 × 109
Variance1367315.4
MonotonicityNot monotonic
2023-01-27T01:46:31.020403image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7001 322
 
0.1%
7066 252
 
0.1%
6697 177
 
< 0.1%
6840 176
 
< 0.1%
6654 174
 
< 0.1%
6696 174
 
< 0.1%
6732 169
 
< 0.1%
6673 169
 
< 0.1%
6734 168
 
< 0.1%
6860 167
 
< 0.1%
Other values (6926) 417455
99.5%
ValueCountFrequency (%)
0 1
< 0.1%
44 1
< 0.1%
47 1
< 0.1%
50 1
< 0.1%
744 1
< 0.1%
936 1
< 0.1%
3616 1
< 0.1%
3621 1
< 0.1%
3671 1
< 0.1%
3675 1
< 0.1%
ValueCountFrequency (%)
11295 1
< 0.1%
11227 1
< 0.1%
11219 1
< 0.1%
11205 1
< 0.1%
11183 1
< 0.1%
11153 1
< 0.1%
11150 1
< 0.1%
11146 1
< 0.1%
11138 1
< 0.1%
11131 1
< 0.1%

Interactions

2023-01-27T01:46:26.215464image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:13.523823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:15.161479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:16.726822image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:18.383948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:19.977627image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:21.527529image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:23.165861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:24.675672image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:26.387136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:13.711165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:15.331814image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:16.899566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:18.557245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:20.154343image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:21.699850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:23.335037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:24.847855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:26.555900image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:13.887486image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:15.503405image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:17.078687image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:18.731250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:20.328949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:21.872018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:23.505924image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:25.025283image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:26.726059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:14.073989image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:15.678823image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:17.255878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:18.906384image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:20.505359image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:22.045425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:23.683899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:25.214562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:26.888230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:14.239025image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:15.850782image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:17.430117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:19.077836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:20.671731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:22.229126image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:23.847458image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:25.390663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:27.072514image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:14.412425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:16.031821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:17.615141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:19.258678image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:20.847046image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:22.416369image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:24.022899image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:25.561873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:27.237793image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:14.640869image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:16.205138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:17.786710image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:19.439890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:21.015316image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:22.672888image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:24.182787image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:25.722898image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:27.402313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:14.801377image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:16.375277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:17.951945image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:19.638113image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:21.184859image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:22.832000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:24.343041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:25.884394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:27.568484image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:14.976276image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:16.553512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:18.207817image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:19.810175image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:21.354347image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:23.000635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:24.510733image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-27T01:46:26.047886image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-27T01:46:31.133683image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Consumption_MWCoal_MWGas_MWHidroelectric_MWNuclear_MWWind_MWSolar_MWBiomass_MWProduction_MW
Consumption_MW1.0000.5470.5570.4200.0920.0680.1370.0380.891
Coal_MW0.5471.0000.304-0.0000.105-0.345-0.049-0.3970.440
Gas_MW0.5570.3041.000-0.1930.2410.162-0.0560.1690.537
Hidroelectric_MW0.420-0.000-0.1931.000-0.195-0.1680.1860.1200.470
Nuclear_MW0.0920.1050.241-0.1951.000-0.000-0.215-0.2000.098
Wind_MW0.068-0.3450.162-0.168-0.0001.000-0.0120.4640.249
Solar_MW0.137-0.049-0.0560.186-0.215-0.0121.0000.1140.142
Biomass_MW0.038-0.3970.1690.120-0.2000.4640.1141.0000.229
Production_MW0.8910.4400.5370.4700.0980.2490.1420.2291.000

Missing values

2023-01-27T01:46:27.839141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-27T01:46:28.144482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Consumption_MWCoal_MWGas_MWHidroelectric_MWNuclear_MWWind_MWSolar_MWBiomass_MWProduction_MW
Date
12624876605302.01754.01144.01391.0706.00.00.00.04995.0
12624882005318.01777.01145.01468.0708.00.00.00.05097.0
12624888005268.01743.01139.01361.0708.00.00.00.04951.0
12624894005358.01759.01142.01449.0707.00.00.00.05057.0
12624900605327.01764.01142.01417.0709.00.00.00.05031.0
12624906605307.01771.01142.01418.0706.00.00.00.05037.0
12624912605256.01752.01153.01368.0712.00.00.00.04985.0
12624924005308.01762.01151.01461.0709.00.00.00.05083.0
12624930005426.01785.01153.01515.0704.00.00.00.05157.0
12624936605340.01782.01150.01488.0706.00.00.00.05126.0
Consumption_MWCoal_MWGas_MWHidroelectric_MWNuclear_MWWind_MWSolar_MWBiomass_MWProduction_MW
Date
15149424657307.02328.0955.01311.01404.02269.0-1.043.08308.0
15149430557295.02250.0941.01335.01404.02244.0-1.044.08217.0
15149436457272.02253.0946.01395.01404.02205.0-1.045.08246.0
15149442357266.02239.0946.01387.01406.02204.0-1.044.08224.0
15149448257287.02275.0945.01425.01401.02193.0-1.045.08283.0
15149454157262.02279.0942.01444.01403.02175.0-1.045.08287.0
15149460057167.02259.0943.01383.01405.02174.0-1.043.08207.0
15149465957122.02251.0945.01362.01405.02159.0-1.045.08165.0
15149471857264.02288.0944.01454.01406.02132.0-1.045.08268.0
15149477757115.02255.0944.01370.01404.02131.0-1.041.08145.0

Duplicate rows

Most frequently occurring

Consumption_MWCoal_MWGas_MWHidroelectric_MWNuclear_MWWind_MWSolar_MWBiomass_MWProduction_MW# duplicates
356588.02593.0515.02476.01421.0-1.00.00.07001.0195
316482.02460.0277.02941.01383.03.00.00.07066.0114
677411.02160.01842.01315.01423.0610.00.00.07349.034
396687.03103.0783.0932.01373.0253.00.00.06446.028
747672.02026.0826.02557.0694.02575.0468.069.09216.018
878211.03294.02203.01579.01424.0-1.043.033.08573.018
647331.02428.01516.01590.01406.0269.0197.056.07462.015
868182.03167.01733.01793.01393.0538.022.046.08686.011
376667.02616.0522.02525.01414.09.00.00.07088.010
587048.03521.0481.01287.01407.0300.00.00.06996.010